When a production issue arises, engineers often face information overload: spreadsheets of pipeline measurements, PDF and PPT discovery reports, technical articles, scans of failed parts, and siloed knowledge bases. The challenge isn’t the absence of data but the difficulty of connecting these disparate pieces into a single coherent explanation.
The Root Cause Intelligence agent eliminates this bottleneck. It unifies structured tabular data (manufacturing pipeline metrics, quality and measurement databases), unstructured content (reports, domain articles, prior case studies), visual inputs (photos, microscope scans, SEM images), and entity relationships (knowledge graphs of parts, materials, and processes).
Corvic’s multi-space retrieval engine decomposes a question like “What is the main cause for this failure?” into targeted sub-queries across each data type. It correlates sensor anomalies with documented failure modes, retrieves supporting evidence from historical cases, and validates insights against visual analysis.
By shifting from manual root-cause investigations to agent-driven automation, manufacturers can cut analysis time from weeks to hours, reduce downtime, and minimize cost of quality. The result: proactive problem-solving, safer operations, and faster decision-making.